# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import annotations

import itertools
import unittest
from functools import partial

import numpy as np
from program_config import ProgramConfig, TensorConfig
from trt_layer_auto_scan_test import TrtLayerAutoScanTest

import paddle.inference as paddle_infer


class TrtConvertEmbEltwiseLayernormTest1(TrtLayerAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        return True

    def sample_program_configs(self):
        def generate_input(batch, input_size):
            return np.random.randint(0, 7, size=(batch, input_size, 1)).astype(
                np.int64
            )

        def generate_weight1(size11, size2):
            return np.random.randn(size11, size2).astype(np.float32)

        def generate_weight2(size12, size2):
            return np.random.randn(size12, size2).astype(np.float32)

        def generate_weight3(size13, size2):
            return np.random.randn(size13, size2).astype(np.float32)

        def generate_weight4(size2):
            return np.random.randn(size2).astype(np.float32)

        for (
            input_size,
            batch,
            size1,
            size2,
            norm_axis,
            epsilon,
            axis1,
            axis2,
            type,
        ) in itertools.product(
            [16, 128],
            [1, 2, 4],
            [[8, 513, 768], [513, 768, 8], [768, 8, 513]],
            [32, 768],
            [2],
            [0.0001, 0.0005],
            [0, -1],
            [0, -1],
            ["lookup_table", "lookup_table_v2"],
        ):
            size11 = size1[0]
            size12 = size1[1]
            size13 = size1[2]
            dics = [
                {
                    "is_sparse": False,
                    "is_distributed": False,
                    "padding_idx": -1,
                    "is_test": True,
                },
                {
                    "is_sparse": False,
                    "is_distributed": False,
                    "padding_idx": -1,
                },
                {"axis": axis1},
                {"axis": axis2},
                {
                    "begin_norm_axis": norm_axis,
                    "epsilon": epsilon,
                },
            ]
            ops_config = [
                {
                    "op_type": type,
                    "op_inputs": {
                        "Ids": ["input_data1"],
                        "W": ["embedding1_weight"],
                    },
                    "op_outputs": {"Out": ["embedding1_output"]},
                    "op_attrs": (
                        dics[0] if type == "lookup_table" else dics[1]
                    ),
                },
                {
                    "op_type": type,
                    "op_inputs": {
                        "Ids": ["input_data2"],
                        "W": ["embedding2_weight"],
                    },
                    "op_outputs": {"Out": ["embedding2_output"]},
                    "op_attrs": (
                        dics[0] if type == "lookup_table" else dics[1]
                    ),
                },
                {
                    "op_type": type,
                    "op_inputs": {
                        "Ids": ["input_data3"],
                        "W": ["embedding3_weight"],
                    },
                    "op_outputs": {"Out": ["embedding3_output"]},
                    "op_attrs": (
                        dics[0] if type == "lookup_table" else dics[1]
                    ),
                },
                {
                    "op_type": "elementwise_add",
                    "op_inputs": {
                        "X": ["embedding2_output"],
                        "Y": ["embedding3_output"],
                    },
                    "op_outputs": {"Out": ["elementwise_add1_output"]},
                    "op_attrs": dics[2],
                },
                {
                    "op_type": "elementwise_add",
                    "op_inputs": {
                        "X": ["elementwise_add1_output"],
                        "Y": ["embedding1_output"],
                    },
                    "op_outputs": {"Out": ["elementwise_add2_output"]},
                    "op_attrs": dics[3],
                },
                {
                    "op_type": "layer_norm",
                    "op_inputs": {
                        "X": ["elementwise_add2_output"],
                        "Bias": ["layer_norm_bias"],
                        "Scale": ["layer_norm_scale"],
                    },
                    "op_outputs": {
                        "Y": ["layer_norm_output1"],
                        "Mean": ["layer_norm_output2"],
                        "Variance": ["layer_norm_output3"],
                    },
                    "op_attrs": dics[4],
                },
            ]
            ops = self.generate_op_config(ops_config)

            program_config = ProgramConfig(
                ops=ops,
                weights={
                    "embedding1_weight": TensorConfig(
                        data_gen=partial(
                            generate_weight1,
                            size11,
                            size2,
                        )
                    ),
                    "embedding2_weight": TensorConfig(
                        data_gen=partial(
                            generate_weight2,
                            size12,
                            size2,
                        )
                    ),
                    "embedding3_weight": TensorConfig(
                        data_gen=partial(
                            generate_weight3,
                            size13,
                            size2,
                        )
                    ),
                    "layer_norm_bias": TensorConfig(
                        data_gen=partial(
                            generate_weight4,
                            size2,
                        )
                    ),
                    "layer_norm_scale": TensorConfig(
                        data_gen=partial(
                            generate_weight4,
                            size2,
                        )
                    ),
                },
                inputs={
                    "input_data1": TensorConfig(
                        data_gen=partial(
                            generate_input,
                            batch,
                            input_size,
                        )
                    ),
                    "input_data2": TensorConfig(
                        data_gen=partial(
                            generate_input,
                            batch,
                            input_size,
                        )
                    ),
                    "input_data3": TensorConfig(
                        data_gen=partial(
                            generate_input,
                            batch,
                            input_size,
                        )
                    ),
                },
                outputs=["layer_norm_output1"],
            )

            yield program_config

    def sample_predictor_configs(
        self, program_config
    ) -> tuple[paddle_infer.Config, list[int], float]:
        def generate_dynamic_shape(attrs):
            self.dynamic_shape.min_input_shape = {
                "input_data1": [1, 4, 1],
                "input_data2": [1, 4, 1],
                "input_data3": [1, 4, 1],
            }
            self.dynamic_shape.max_input_shape = {
                "input_data1": [4, 512, 1],
                "input_data2": [4, 512, 1],
                "input_data3": [4, 512, 1],
            }
            self.dynamic_shape.opt_input_shape = {
                "input_data1": [2, 128, 1],
                "input_data2": [2, 128, 1],
                "input_data3": [2, 128, 1],
            }

        def clear_dynamic_shape():
            self.dynamic_shape.max_input_shape = {}
            self.dynamic_shape.min_input_shape = {}
            self.dynamic_shape.opt_input_shape = {}

        attrs = [
            program_config.ops[i].attrs for i in range(len(program_config.ops))
        ]

        # for static_shape
        clear_dynamic_shape()
        self.trt_param.precision = paddle_infer.PrecisionType.Float32
        program_config.set_input_type(np.float32)
        yield self.create_inference_config(), (0, 5), 1e-5
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        program_config.set_input_type(np.float16)
        yield self.create_inference_config(), (0, 5), 2e-2

        # for dynamic_shape
        generate_dynamic_shape(attrs)
        self.trt_param.precision = paddle_infer.PrecisionType.Float32
        program_config.set_input_type(np.float32)
        yield self.create_inference_config(), (1, 4), 1e-5
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        program_config.set_input_type(np.float16)
        yield self.create_inference_config(), (1, 4), 2e-2

    def test(self):
        self.run_test()


if __name__ == "__main__":
    unittest.main()
